Spatial patterns and temporal variability of drought in Western Iran

An analysis of drought in western Iran from 1966 to 2000 is presented using monthly precipitation data observed at 140 gauges uniformly distributed over the area. Drought conditions have been assessed by means of the Standardized Precipitation Index (SPI). To study the long-term drought variability the principal component analysis was applied to the SPI field computed on 12-month time scale. The analysis shows that applying an orthogonal rotation to the first two principal component patterns, two distinct sub-regions having different climatic variability may be identified. Results have been compared to those obtained for the largescale using re-analysis data suggesting a satisfactory agreement. Furthermore, the extension of the large-scale analysis to a longer period (1948–2007) shows that the spatial patterns and the associated time variability of drought are subjected to noticeable changes. Finally, the relationship between hydrological droughts in the two sub-regions and El Niño Southern Oscillation events has been investigated finding that there is not clear evidence for a link between the two phenomen

Analysis of Droughts of Northwest of Iran Using the Reconnaissance Drought Index

Introduction: Drought is a creeping natural phenomenon, which can occur in any region. Such phenomenon not only affects the region subjected to drought, but its adverse effects can also be extended to other adjacent regions. This phenomenon mainly starts with water deficiency (say less than long- term mean of variable under study such as rainfall, streamflow, groundwater level or soil moisture) and progress in time. This period can be ended by increasing the rainfall and reaching the mean level. Even after the ending of a drought period, its adverse effects can be continued for several months. Although, it is not possible (at least at this time) to prevent the occurrence of drought in a given region, it is not impossible to alleviate the drought consequences by scientific water management. Such a management should be employed before drought initiation as well as during it and continue on even after the end of the drought period. The frequency of the main drought characteristics is a major concern of this study. The Northwest of Iran recently encountered severe and prolonged droughts, such that a major portion of the Urmia Lake surface disappeared during the last drought in recent years. In order to study drought characteristics, we used the Reconnaissance Drought Index (RDI). This index is based on annual rainfall and potential reference crop evapotranspiration (abbreviated by PET here). This study employed the Monte Carlo simulation technique for synthetic data generation for analysis. Materials and Methods: The information from the 17 synoptic weather stations located in the North-west of Iran was used for drought analysis. Data was gathered from the Islamic Republic of Iran’s Meteorological Organization (IRIMO). In the first stage of research, the ratio of long term mean annual precipitation to evapotranspiration was calculated for each of the stations. For this purpose, the Penman-Montheis (FAO 56) method was selected for PET estimation. In the second stage, the 64 candidate statistical distributions were fitted for the mentioned RDI’s of each station. The best statistical distribution was selected among the 64 candidate distributions. The best fitted distribution was identified by the chi-square criterion. The parameters of the distribution were estimated by the Maximum Likelihood Estimation (MLE) scheme. Then 500 synthetic time series (each of them have the same number of observed data) were generated employing the parent population parameters. The three main drought characteristics (namely duration, severity and magnitude) were obtained for each of the mentioned artificial time series. The maximum values for each of the mentioned drought characteristic were selected for each year. Then, a new time series having the 500 elements were obtained by collecting the chosen values for each station. Once again the best distribution was selected for each series. Drought characteristics for different return periods (2, 10, 25, 50, 100 and 200 years) were estimated for each station. Results and Discussion: Preliminary results indicated that a negative trend existed in annual rainfall time series for almost all of the stations. On the other hand, the pattern of monthly PET histograms were more or less similar for all of the selected stations. The peak of the PET was mainly observed in the hottest month of year, whereas the lowest value of the monthly PET belonged to the coldest month of year. The results showed that the amount of annual rainfall time series decreases sharply, after the year 1991. However, PET values significantly increase for all of the selected stations. After calculation of RDI values, the histogram of annual RDI’s was plotted against the year. This is repeated for all of the selected stations. Figure. 6 shows the mentioned diagram for Tabriz station as an example. In the mentioned Figure, negative values of RDI (shown by red bars) indicated the drought years. A critical prolonged drought with a sixteen years duration period (neglecting the 2001 in which RDI value was a small positive value) was experienced in Tabriz. The maximum drought severity in Tabriz was estimated to be about -7 in RDI units. Urmia station experienced the longest drought period, starting from 1995 and ending in 2005. It can be concluded that although few sparse wet years were observed in some of the selected stations in the studied period, they cannot compensate the water deficiency accumulated during several consecutive years. The results showed that the lowest value of the ratio of drought severity in a 100 year return period to the corresponding value for 2 year return period was about 2.13 (belonged to the Tabriz station), whereas the highest value was 3.17 (belonged to the Tekab station). On the other hand, the lowest value for the ratio of drought duration in 100 year return period to its corresponding value for 2 year return period was 1.95 (experienced in the Makoo station). The highest mentioned ratio was 9.18 (observed in the Sardasht station). The lowest and highest value of the ratio of drought magnitude in 100 year return period to its corresponding value for 2 year return period were 1.17 and 2.74, respectively. The mentioned drought magnitude ratios were observed in the Urmia and the Khalkhal stations, respectively. The isoplethes of the three main drought characteristics (severity, magnitude, duration) for a 10 year return period was illustrated for the study area (Northwest of Iran). Conclusion: In the present study RDI values were used to analyze drought characteristics of Northwest of Iran. The Penman-Montheis method was used to estimate PET (needed for RDI) values of the stations. The main three drought characteristics were calculated for each of the 500 synthetic time series. The results showed that nearly all of the areas under study experienced severe and prolonged droughts. It can be concluded that a sharp decrease in annual precipitation as well as the increase in PET (due to greenhouse effects of consuming fossil fuels as the main source of energy in the region) from 1995 to 2005 was observed in the study area. Scientific management of available water in the study area is extremely vital to alleviate the adverse consequences of drought. Several economic and social problems were anticipated in these arid and semi-arid regions of Iran

Forecasting of Mean Daily Runoff Discharge of Behesht-Abad River Using Wavelet Analysis

Forecasting of river discharge is a key aspect of efficient water resources planning and management. In this study, two models based on Wavelet Analysis and Artificial Neural networks (ANNs) were developed for forecasting discharge of Behesht-Abad River. For this purpose, mean daily discharge data of mentioned river as well as precipitation data of 17 meteorological stations were used in the period 1999-2008. In the first method, called Cross Wavelet (CW), complex Morlet wavelet was used as analyzer function. Wavelet analyzing was performed for every daily rainfall and average discharge time series, separately. Initial phase, phase differences of subseries obtained from wavelet analysis, and calibration coefficients were calculated. Then structural series were reconstructed and average of structural components calculated. The river discharges were predicted for 1, 2, 3 and 7 days ahead forecasting horizon. In the second method, called Wavelet Neural Networks conjunction (WNN), a preprocessing was done on the initial input matrix using Meyer wavelet. Then the elements of the initial input matrix were normalized and the second input matrix was created. A three layer Feed Forward Back Propagation (FFBP) was formed based on the second input matrix and target matrix. After training the model using Levenberg–Marquardt (LM) algorithm, the river discharges were predicted for short term time horizons. The results showed that the WNN method had higher accuracy in short-term forecasting of river discharge in comparison with CW and ANN methods

River Flow Prediction Using the Nearest Neighbor Probabilistic Ensemble Method

Introduction: In the recent years, researchers interested on probabilistic forecasting of hydrologic variables such river flow.A probabilistic approach aims at quantifying the prediction reliability through a probability distribution function or a prediction interval for the unknown future value. The evaluation of the uncertainty associated to the forecast is seen as a fundamental information, not only to correctly assess the prediction, but also to compare forecasts from different methods and to evaluate actions and decisions conditionally on the expected values. Several probabilistic approaches have been proposed in the literature, including (1) methods that use resampling techniques to assess parameter and model uncertainty, such as the Metropolis algorithm or the Generalized Likelihood Uncertainty Estimation (GLUE) methodology for an application to runoff prediction), (2) methods based on processing the forecast errors of past data to produce the probability distributions of future values and (3) methods that evaluate how the uncertainty propagates from the rainfall forecast to the river discharge prediction, as the Bayesian forecasting system. Materials and Methods: In this study, two different probabilistic methods are used for river flow prediction.Then the uncertainty related to the forecast is quantified. One approach is based on linear predictors and in the other, nearest neighbor was used. The nonlinear probabilistic ensemble can be used for nonlinear time series analysis using locally linear predictors, while NNPE utilize a method adapted for one step ahead nearest neighbor methods. In this regard, daily river discharge (twelve years) of Dizaj and Mashin Stations on Baranduz-Chay basin in west Azerbijan and Zard-River basin in Khouzestan provinces were used, respectively. The first six years of data was applied for fitting the model. The next three years was used to calibration and the remained three yeas utilized for testing the models. Different combinations of recorded data were used as the input pattern to streamflow forecasting. Results and Discussion: Application of the used approaches in ensemble form (in order to choice the optimized parameters) improved the model accuracy and robustness in prediction. Different statistical criteria including correlation coefficient (R), root mean squared error (RMSE) and Nash–Sutcliffe efficiency coefficient (E) were used for evaluating the performance of models. The ranges of parameter values to be covered in the ensemble prediction have been identified by some preliminary tests on the calibration set. Since very small values of k have been found to produce unacceptable results due to the presence of noise, the minimum value is fixed at 100 and trial values are taken up to 10000 (k = 100, 200, 300,500, 1000, 2000, 5000, 10000). The values of mare chosen between 1 and 20 and delay time values γ are tested in the range [1,5]. With increasing the discharge values, the width of confidence band increased and the maximum confidence band is related to maximum river flows. In Dizaj station, for ensemble numbers in the range of 50-100, the variation of RMSE is linear. The variation of RMSE in Mashin station is linear for ensemble members in the range of 100-150. It seems the numbers of ensemble members equals to 100 is suitable for pattern construction. The performance of NNPE model was acceptable for two stations. The number of points excluded 95% confidence interval were equal to 108 and 96 for Dizaj and Mashin stations, respectively. The results showed that the performance of model was better in prediction of minimum and median discharge in comparing maximum values. Conclusion: The results confirmed the performance and reliability of applied methods. The results indicated the better performance and lower uncertainty of ensemble method based on nearest neighbor in comparison with probabilistic nonlinear ensemble method. Nash–Sutcliffe model efficiency coefficient (E) for nearest neighbor probabilistic ensemble method in Dizaj and Mashin Stations during test period of model obtained 0.91 and 0.93, respectively.The investigation on the performance of models in different basins showed that the models have better performance in Zard river basin compared to Baranduz-Chaybasin. Furthermore the variation of discharge values during test period in Zard basin was lower in comparison of Baranduz-Chay basin. The real advantage of including streamflow forecasts requires detailed and specific investigations, but the preliminary results suggest the good potentiality of probabilistic NLP method. Using ensemble prediction method can help to decision makers in order to determine the uncertainty of prediction in water resources field

Hybrydowy model wavelet-SARIMA-ANN do prognozowania opadów

Given its importance in water resources management, particularly in terms of minimizing flood or drought hazards, precipitation forecasting has seen a wide variety of approaches tested. As monthly precipitation time series have nonlinear features and multiple time scales, wavelet, seasonal auto regressive integrated moving average (SARIMA) and hybrid artificial neural network (ANN) methods were tested for their ability to accurately predict monthly precipitation. A 40-year (1970–2009) precipitation time series from Iran’s Nahavand meteorological station (34°12’N lat., 48°22’E long.) was decomposed into one low frequency subseries and several high frequency sub-series by wavelet transform. The low frequency sub-series were predicted with a SARIMA model, while high frequency subseries were predicted with an ANN. Finally, the predicted subseries were reconstructed to predict the precipitation of future single months. Comparing model-generated values with observed data, the wavelet-SARIMA-ANN model was seen to outperform wavelet-ANN and wavelet-SARIMA models in terms of precipitation forecasting accuracy.Prognozowanie opadów, ze względu na ich znaczenie w gospodarce zasobami wodnymi, szczególnie w zmniejszaniu ryzyka powodzi czy susz, było już przedmiotem wielu badań. Serie miesięcznych opadów mają właściwości nieliniowe i różne skale czasowe, w związku z czym przetestowano różne metody: wavelet, metodę zintegrowanej sezonowej autoregresji z ruchomą średnią (SARIMA) i hybrydową metodę sztucznych sieci neuronowych (ANN) pod kątem ich zdolności do dokładnego przewidywania miesięcznych opadów. Czterdziestoletnią (1970–2009) serię opadów z irańskiej stacji meteorologicznej w Nahavand (34°12’N, 48°22’E) rozłożono na jedną podserię o niskiej częstotliwości i kilka podserii o wysokiej częstotliwości występowania opadów przez transformację falkową. Podserie o niskiej częstotliwości prognozowano za pomocą modelu SARIMA, podczas gdy podserie o wysokiej częstotliwości prognozowano, stosując ANN. Na koniec prognozowane podserie zrekonstruowano celem przewidywania opadów w poszczególnych miesiącach w przyszłości. Porównanie wartości generowanych przez model z danymi z obserwacji wykazało lepszą dokładność prognozowania opadów za pomocą modelu wavelet-SARIMA-ANN niż za pomocą modeli wavelet-ANN i wavelet-SARIMA

Operation of two major reservoirs of Iran under IPCC scenarios during the XXI Century.

We assess the effects of prospective climate change until 2100 on water management of two major reservoirs of Iran, namely, Dez (3.34x10(9)m(3)) and Alavian (6x10(7)m(3)). We tune the Poly-Hydro model suited for simulation of hydrological cycle in high altitude snow-fed catchments. We assess optimal operation rules (ORs) for the reservoirs using three algorithms under dynamic and static operation and linear and non-linear decision rules during control run (1990-2010 for Dez and 2000-2010 for Alavian). We use projected climate scenarios (plus statistical downscaling) from three general circulation models, EC-Earth, CCSM4, and ECHAM6, and three emission scenarios, or representative concentration pathways (RCPs), RCP2.6, RCP4.5, and RCP8.5, for a grand total of nine scenarios, to mimic evolution of the hydrological cycle under future climate until 2100. We subsequently test the ORs under the future hydrological scenarios (at half century and end of century) and the need for reoptimization. Poly-Hydro model when benchmarked against historical data well mimics the hydrological budget of both catchments, including the main processes of evapotranspiration and streamflows. Teaching-learning-based optimization delivers the best performance in both reservoirs according to objective scores and is used for future operation. Our projections in Dez catchment depict decreased precipitation along the XXI century, with -1% on average (of the nine scenarios) at half century and -6% at the end of century, with changes in streamflows on average -7% yearly and -13% yearly, respectively. In Alavian, precipitation would decrease by -10% on average at half century and -13% at the end of century, with streamflows -14% yearly and -18% yearly, respectively. Under the projected future hydrology, reservoirs' operation would provide lower performance (i.e., larger lack of water) than now, especially for Alavian dam. Our results provide evidence of potentially decreasing water availability and less effective water management in water stressed areas like Northern Iran here during this century

Operation of two major reservoirs of Iran under {IPCC} scenarios during the {XXI} Century

We assess the effects of prospective climate change until 2100 on water management of two major reservoirs of Iran, namely, Dez (3.34x10(9)m(3)) and Alavian (6x10(7)m(3)). We tune the Poly-Hydro model suited for simulation of hydrological cycle in high altitude snow-fed catchments. We assess optimal operation rules (ORs) for the reservoirs using three algorithms under dynamic and static operation and linear and non-linear decision rules during control run (1990-2010 for Dez and 2000-2010 for Alavian). We use projected climate scenarios (plus statistical downscaling) from three general circulation models, EC-Earth, CCSM4, and ECHAM6, and three emission scenarios, or representative concentration pathways (RCPs), RCP2.6, RCP4.5, and RCP8.5, for a grand total of nine scenarios, to mimic evolution of the hydrological cycle under future climate until 2100. We subsequently test the ORs under the future hydrological scenarios (at half century and end of century) and the need for reoptimization. Poly-Hydro model when benchmarked against historical data well mimics the hydrological budget of both catchments, including the main processes of evapotranspiration and streamflows. Teaching-learning-based optimization delivers the best performance in both reservoirs according to objective scores and is used for future operation. Our projections in Dez catchment depict decreased precipitation along the XXI century, with -1% on average (of the nine scenarios) at half century and -6% at the end of century, with changes in streamflows on average -7% yearly and -13% yearly, respectively. In Alavian, precipitation would decrease by -10% on average at half century and -13% at the end of century, with streamflows -14% yearly and -18% yearly, respectively. Under the projected future hydrology, reservoirs' operation would provide lower performance (i.e., larger lack of water) than now, especially for Alavian dam. Our results provide evidence of potentially decreasing water availability and less effective water management in water stressed areas like Northern Iran here during this century